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Welcome to the Predictive Scoring Model Breakdown Series.
In this series, we'll be breaking down novel scoring models that you can use to predict expansion opportunities or churn risks for your existing customers using Parative.
Parative is a predictive scoring engine that unifies customer data to identify risk and opportunity, helping teams get the most out of their customer data and focus on growth.
Today, we will focus on a particular type of predictive score designed to track your customers' readiness for a cross-sell to an additional product.
The Cross-Sell Readiness Score.
Want to build models like this one? Check out Parative's Predictive Scoring Engine to learn more 👇🏾
With the correct data and analysis, you can identify the factors influencing customer behavior and make informed decisions about your cross-sell strategy.
This is where Parative comes in.
It provides a comprehensive view of your customer's likelihood to purchase based on critical factors such as product usage, customer segmentation, and engagement with gated product features.
By automating the process of analyzing and responding to customer data, Parative makes it easy to get the most out of your customer data and focus on growth.
In this post, we'll break down the key factors that impact this score and show you how to use Parative to drive your cross-sell strategy.
With clear, concise information and examples, you'll have all the tools you need to build a predictive score that works for your business.
So, let's get started!
The Cross-Sell Readiness Predictive Score Model is ideal for a SaaS business that sells multiple product lines and is looking to grow its Net Revenue Retention by expanding the LTV of existing accounts.
The model is a combination of conditions and points determining the likelihood of a customer's cross-selling.
In this model, each condition is assigned a value of either "Required Condition," "Promoter Condition," "Point Condition," or "Disqualifying Condition."
Then, the conditions and their values are used to calculate the final score, which ranges from 0 to 100.
In Parative, you can also set "Points Threshold" values and point thresholds to dynamically communicate the state of the score.
If the customer or account has at least as many points as a values point threshold, they will be assigned that value. There are a total of 30 available points in the model.
Let's take a closer look at each type of condition:
Below, we will describe the various conditions of the score - breaking them down into the following categories:
Now that you better understand how Scoring works in Parative, let's break this model down further.
The concept of "Buyer Fit" is critical to the success of any cross-sell initiative. A company may have the best product or solution in the market, but if it is not the right fit for the target customer, the chances of success are greatly reduced.
This is where the High ICP Fit Segment comes into play. By identifying the ideal customer profile for a cross-sell, we can focus our efforts on the customers who are most likely to respond positively and drive value for our business.
High ICP Fit Segment refers to the ideal profile for a cross-sell as determined by firmographic factors and market data about the company. This data can be collected via third parties or through Parative's own Market Data add-on.
A Correlation Matrix is used to analyze this data and determine which factors are most strongly associated with a high likelihood of success for a cross-sell.
A Correlation Matrix is a statistical tool that measures the strength of the relationship between two or more variables. In this case, it measures the strength of the relationship between firmographic factors and market data about the company and the likelihood of success for a cross-sell.
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Using a Correlation Matrix, we can identify which factors have the strongest association with a high likelihood of success. For example, if a company is B2B, SaaS, VC-backed, uses marketing automation, and has a high web traffic quartile, it will likely be a good fit for a cross-sell.
To give you a better understanding of what a High ICP Fit Segment might look like, let's consider the following example:
Imagine a machine learning model that takes into account the following factors:
By using a Correlation Matrix, the model determines that a company with the following characteristics has a high likelihood of success for a cross-sell:
This company would be considered a High ICP Fit Segment and would receive a boost in its cross-sell score as a result.
The Correlation Matrix is a powerful tool for analyzing customer data and determining which factors are most strongly correlated with a customer's likelihood to cross-sell. This tool helps us understand which firmographic and market data points are most indicative of a customer that is a good fit for a cross-sell.
Here's how it works:
Using a Correlation Matrix, we can quickly and effectively identify the key factors most indicative of a customer's likelihood to cross-sell. This information informs our cross-sell strategy and helps us target our efforts to the customers who are most likely to respond positively.
To calculate the High ICP Fit Segment, the data is analyzed for correlations between different factors. These correlations are then used to determine the weight of each factor in the final score. For example, a strong correlation between a company's size and its likelihood to cross-sell would mean that size would be given a higher weight in the final score.
Once the weight of each factor is determined, the final High ICP Fit Segment score is calculated by taking into account all of the firmographic and market data factors. Companies with a score that meets a certain threshold are then classified as being in the High ICP Fit Segment.
This information is then used in the cross-sell predictive score to boost the score of customers that fit the ideal profile. By targeting customers who are most likely to be a good fit for a cross-sell, the cross-sell score helps SaaS businesses increase the lifetime value of their existing accounts.
In the next section, we'll delve deeper into the Customer Requirements aspect of the cross-sell score.
In addition to the High ICP Fit Segment, the cross-sell score also considers the customer's requirements. These requirements are factors that are crucial for a customer to be regarded as a good fit for a cross-sell.
Four main customer requirements are evaluated as part of the cross-sell score:
One of the critical requirements for a customer to be considered a good fit for a cross-sell is whether they have the company's flagship product. This requirement is straightforward, but it is important to evaluate because having the flagship product indicates that the customer is already invested in the company's offerings and is more likely to be receptive to additional products.
The customer's health score is also evaluated as part of the cross-sell score. For example, a green health score indicates that the customer is in good standing and is more likely to be receptive to a cross-sell. On the other hand, a red health score may indicate that the customer is experiencing issues with the company's products or services and is less likely to be receptive to a cross-sell.
A competing product in a customer's tech stack can indicate that they are open to using a combined solution rather than having functions siloed.
If a customer is using a separate tool for a position that your company offers a solution for, they are more likely to be open to being cross-sold.
For example, by incorporating these functions into a single solution, companies can streamline their processes and improve efficiency.
As a result, offering a combined solution is often an attractive proposition for businesses. The presence of a competing product in a customer's tech stack indicates that they may be open to considering your company's offerings.
In the cross-sell predictive score, the presence of a competing product in a customer's tech stack is given a high weight, as it is considered a strong indicator of the customer's likelihood to be open to cross-selling
In the next section, we'll take a closer look at the Engaged Gated Product Features aspect of the cross-sell score.
As a SaaS business, it's important to understand what your customers are using your product for and how they interact with it and with your website. This information can provide valuable insights into which customers are more likely to respond positively to a cross-sell.
One key indicator of a customer's interest in your company and products is their behavior on your website.
If a customer is exhibiting high buying intent, such as spending a lot of time on product pages and exploring different features, they are likely to consider purchasing.
A customer with a high buying intent score is a good candidate for a cross-sell.
Another important aspect to consider is the level of engagement a customer has with your product. If a customer is an active decision-maker who uses your product regularly and finds value in it, they are more likely to be open to a cross-sell. This customer behavior is important in determining the likelihood of a successful cross-sell.
Gated functionality, such as Single Sign On (SSO) documentation, can also provide valuable insights into a customer's level of engagement with your product.
If a customer is using gated functionality, it means they are taking the time to explore and understand the more advanced features of your product. This level of engagement is a good indicator that the customer is finding value in your product and is likely to respond positively to a cross-sell.
In the next section, we'll discuss the final aspect of the cross-sell score: Customer Risks.
When it comes to cross-selling, certain red flags indicate a customer may need more time to be ready for an upsell.
For the sake of this breakdown, we will call these factors "Customer Risks."
Understanding these risks is crucial for developing a successful cross-sell strategy.
A customer that is not actively using the product is a clear indication that they may not be interested in an upsell. These customers may not be delighted with the product or simply not use it to its full potential.
In either case, it's essential to understand why the customer is not actively using the product before attempting a cross-sell.
When a decision maker within a company is unhappy with the product, it's unlikely they will be interested in an upsell.
These customers may have specific pain points that need to be addressed before they are open to a cross-sell. Understanding the source of the customer's unhappiness is key to developing a successful cross-sell strategy.
Customers that ar in a renewal period may not be open to a cross-sell as they may be focused on renewing their existing contract. These customers may also reassess their current product usage and consider alternative solutions. Understanding the customer's priorities during a renewal period is important before attempting a cross-sell.
Customers that are in the onboarding phase are likely to be focused on getting up and running with the product. They may still need to be thoroughly familiar with the product and its capabilities and may not be ready for an upsell. Therefore, waiting until the customer is fully onboarded and familiar with the product is important before attempting a cross-sell.
As you can see, customer risks are essential to a customer's cross-sell readiness.
Understanding these risks and addressing any potential issues is crucial for developing a successful cross-sell strategy.
In the next section, we'll summarize the key takeaways from this guide and provide some final thoughts on cross-selling.
A SaaS company selling a suite of productivity tools experienced a challenge with cross-selling their solutions to existing customers. The company had a large customer base but found it challenging to identify which customers would most likely be receptive to cross-selling efforts.
To address this challenge, the company implemented Parative's cross-sell scoring model, which leverages firmographic factors and market data to identify the ideal profile for a cross-sell. Using a Correlation Matrix, the company analyzed customer data and determined which factors were most strongly associated with a high likelihood of success for a cross-sell.
The cross-sell scoring model took into account the following factors:
The company found that customers who fit the ideal profile, as determined by the scoring model, were more likely to be receptive to cross-selling efforts.
The company also utilized Parative's real-time alerting and automation functionality to target these customers with cross-sell offers at the right time.
As a result of using the cross-sell scoring model, the company was able to increase the lifetime value of its existing accounts. The company saw a significant boost in its cross-selling efforts, resulting in higher customer satisfaction and increased revenue. The company grew its business and achieved its goals through a more targeted and effective cross-selling strategy.
Cross-selling is a vital component of SaaS revenue growth, and cross-sell scoring is an indispensable tool for predicting and driving cross-selling success.
In this post, we've covered the key elements of a cross-sell score, how to set it up and operationalize it, and how to leverage it to drive cross-selling success.
You can prioritize cross-selling in your organization by tracking key metrics, using effective tactics, utilizing the right tools and technologies, and driving results with data-driven insights and actionable advice.
Stay tuned for more posts in this series, where we'll continue to explore innovative scoring models and how they can help grow your SaaS business.
